Back in the day you had to optimize your card for Quake, do everything to make it run well. Now you have to do that for Pytorch.
That is exactly the attitude that got AMD out in the cold away from the AI revolution; they learned a lot of stupid lessons about optimising to specific games and present-day use cases instead of trying to implement general capabilities to a higher standard like Nvidia did in CUDA. They ended up a decade away from a multi-trillion dollar market
PyTorch might be special. I wouldn't be at all surprised if AMD does have a dedicated engineer working on PyTorch. But their problem to date hasn't been that their engagement with PyTorch, but rather that literally nobody could make PyTorch work on AMD cards which had buggy and terrible support for GPGPU work. If they fixed that some random might do the work without their involvement because a lot of people want to see that happen.
Considering its importance, it shouldn't be one engineer. It should be 50+.
In theory they might not even need to be involved in optimising compute kernels, there is probably some PhD student who'll do the work because they want to be a kernel-optimising specialist. In practice a few strategic applications of paid talent is all they really need to do. Everyone wants to diversify off Nvidia so there is a lot of interest in supporting AMD if they are willing to push out firmware that multiplies matrices without crashing. Which has been a weird sticking point for AMD for a surprising amount of time.